def load_and_reduce(filename, add_noise=False, rms_noise=0.001,
nsig=3):
'''
Load the cube in and derive the property arrays.
'''
if add_noise:
if rms_noise is None:
raise TypeError("Must specify value of rms noise.")
cube, hdr = getdata(filename, header=True)
from scipy.stats import norm
cube += norm.rvs(0.0, rms_noise, cube.shape)
sc = SpectralCube(data=cube, wcs=WCS(hdr))
mask = LazyMask(np.isfinite, sc)
sc = sc.with_mask(mask)
else:
sc = filename
reduc = Mask_and_Moments(sc, scale=rms_noise)
reduc.make_mask(mask=reduc.cube > nsig * reduc.scale)
reduc.make_moments()
reduc.make_moment_errors()
return reduc.to_dict()
def reduce_and_save(filename, add_noise=False, rms_noise=0.001,
output_path="", cube_output=None,
nsig=3, slicewise_noise=True):
'''
Load the cube in and derive the property arrays.
'''
if add_noise:
if rms_noise is None:
raise TypeError("Must specify value of rms noise.")
cube, hdr = getdata(filename, header=True)
# Optionally scale noise by 1/10th of the 98th percentile in the cube
if rms_noise == 'scaled':
rms_noise = 0.1*np.percentile(cube[np.isfinite(cube)], 98)
from scipy.stats import norm
if not slicewise_noise:
cube += norm.rvs(0.0, rms_noise, cube.shape)
else:
spec_shape = cube.shape[0]
slice_shape = cube.shape[1:]
for i in range(spec_shape):
cube[i, :, :] += norm.rvs(0.0, rms_noise, slice_shape)
sc = SpectralCube(data=cube, wcs=WCS(hdr))
mask = LazyMask(np.isfinite, sc)
sc = sc.with_mask(mask)
else:
sc = filename
reduc = Mask_and_Moments(sc, scale=rms_noise)
reduc.make_mask(mask=reduc.cube > nsig * reduc.scale)
reduc.make_moments()
reduc.make_moment_errors()
# Remove .fits from filename
save_name = filename.split("/")[-1][:-4]
reduc.to_fits(output_path+save_name)
# Save the noisy cube too
if add_noise:
if cube_output is None:
reduc.cube.hdu.writeto(output_path+save_name)
else:
reduc.cube.hdu.writeto(cube_output+save_name)
def load_and_reduce(filename,
add_noise=False,
rms_noise=0.001,
nsig=3,
slicewise_noise=True):
'''
Load the cube in and derive the property arrays.
'''
if add_noise:
if rms_noise is None:
raise TypeError("Must specify value of rms noise.")
cube, hdr = getdata(filename, header=True)
# Optionally scale noise by 1/10th of the 98th percentile in the cube
if rms_noise == 'scaled':
rms_noise = 0.1 * np.percentile(cube[np.isfinite(cube)], 98)
from scipy.stats import norm
if not slicewise_noise:
cube += norm.rvs(0.0, rms_noise, cube.shape)
else:
spec_shape = cube.shape[0]
slice_shape = cube.shape[1:]
for i in range(spec_shape):
cube[i, :, :] += norm.rvs(0.0, rms_noise, slice_shape)
sc = SpectralCube(data=cube, wcs=WCS(hdr))
mask = LazyMask(np.isfinite, sc)
sc = sc.with_mask(mask)
else:
sc = filename
reduc = Mask_and_Moments(sc, scale=rms_noise)
reduc.make_mask(mask=reduc.cube > nsig * reduc.scale)
reduc.make_moments()
reduc.make_moment_errors()
return reduc.to_dict()
def load_and_reduce(filename, add_noise=False, rms_noise=0.001,
nsig=3, slicewise_noise=True):
'''
Load the cube in and derive the property arrays.
'''
if add_noise:
if rms_noise is None:
raise TypeError("Must specify value of rms noise.")
cube, hdr = getdata(filename, header=True)
# Optionally scale noise by 1/10th of the 98th percentile in the cube
if rms_noise == 'scaled':
rms_noise = 0.1*np.percentile(cube[np.isfinite(cube)], 98)
from scipy.stats import norm
if not slicewise_noise:
cube += norm.rvs(0.0, rms_noise, cube.shape)
else:
spec_shape = cube.shape[0]
slice_shape = cube.shape[1:]
for i in range(spec_shape):
cube[i, :, :] += norm.rvs(0.0, rms_noise, slice_shape)
sc = SpectralCube(data=cube, wcs=WCS(hdr))
mask = LazyMask(np.isfinite, sc)
sc = sc.with_mask(mask)
else:
sc = filename
reduc = Mask_and_Moments(sc, scale=rms_noise)
reduc.make_mask(mask=reduc.cube > nsig * reduc.scale)
reduc.make_moments()
reduc.make_moment_errors()
return reduc.to_dict()
def reduce_and_save(filename,
add_noise=False,
regrid_linewidth=False,
rms_noise=0.001 * u.K,
output_path="",
cube_output=None,
nsig=3,
slicewise_noise=True):
'''
Load the cube in and derive the property arrays.
'''
if add_noise or regrid_linewidth:
sc = SpectralCube.read(filename)
if add_noise:
if rms_noise is None:
raise TypeError("Must specify value of rms noise.")
cube = sc.filled_data[:].value
# Optionally scale noise by 1/10th of the 98th percentile in the
# cube
if rms_noise == 'scaled':
rms_noise = 0.1 * \
np.percentile(cube[np.isfinite(cube)], 98) * sc.unit
from scipy.stats import norm
if not slicewise_noise:
cube += norm.rvs(0.0, rms_noise.value, cube.shape)
else:
spec_shape = cube.shape[0]
slice_shape = cube.shape[1:]
for i in range(spec_shape):
cube[i, :, :] += norm.rvs(0.0, rms_noise.value,
slice_shape)
sc = SpectralCube(data=cube * sc.unit,
wcs=sc.wcs,
meta={"BUNIT": "K"})
mask = LazyMask(np.isfinite, sc)
sc = sc.with_mask(mask)
if regrid_linewidth:
# Normalize the cubes to have the same linewidth
# channels_per_sigma=20 scales to the largest mean line width in
# SimSuite8 (~800 km/s; Design 22). So effectively everything is
# "smoothed" to have this line width
# Intensities are normalized by their 95% value.
sc = preprocessor(sc,
min_intensity=nsig * rms_noise,
norm_intensity=True,
norm_percentile=95,
channels_per_sigma=20)
else:
sc = filename
# Run the same signal masking procedure that was used for the
# COMPLETE cubes
if add_noise:
# The default settings were set based on these cubes
sc = make_signal_mask(sc)[0]
reduc = Mask_and_Moments(sc, scale=rms_noise)
if not add_noise:
reduc.make_mask(mask=reduc.cube > nsig * reduc.scale)
reduc.make_moments()
reduc.make_moment_errors()
# Remove .fits from filename
save_name = os.path.splitext(os.path.basename(filename))[0]
reduc.to_fits(os.path.join(output_path, save_name))
# Save the noisy cube too
if add_noise or regrid_linewidth:
save_name += ".fits"
if cube_output is None:
sc.hdu.writeto(os.path.join(output_path, save_name))
else:
sc.hdu.writeto(os.path.join(cube_output, save_name))
data1, hdr1 = fits.getdata(fits1, header=True)
data2, hdr2 = fits.getdata(fits2, header=True)
if add_noise:
data1 += np.random.normal(0.0, 0.788 / 10, data1.shape)
data2 += np.random.normal(0.0, 0.788 / 10, data2.shape)
cube1 = SpectralCube(data=data1, wcs=WCS(hdr1))
cube1 = cube1.with_mask(LazyMask(np.isfinite, cube1))
cube2 = SpectralCube(data=data2, wcs=WCS(hdr2))
cube2 = cube2.with_mask(LazyMask(np.isfinite, cube2))
set1 = Mask_and_Moments(cube1)
mask = cube1 > 3*set1.scale
set1.make_mask(mask=mask)
set1.make_moments()
set1.make_moment_errors()
dataset1 = set1.to_dict()
set2 = Mask_and_Moments(cube2)
mask = cube2 > 3*set2.scale
set2.make_mask(mask=mask)
set2.make_moments()
set2.make_moment_errors()
dataset2 = set2.to_dict()
# Wavelet Transform
from turbustat.statistics import Wavelet_Distance
